论文标题
符号音乐构图深度学习模型的主观评估
Subjective Evaluation of Deep Learning Models for Symbolic Music Composition
论文作者
论文摘要
通常对深度学习模型进行评估，以衡量和比较其在给定任务上的表现。通常用于评估这些模型的指标是用于不同任务的标准指标。在音乐作品或一代领域，其他领域中使用的标准指标在音乐理论方面没有明确的含义。在本文中，我们提出了一种主观方法来评估基于AI的音乐构图系统，通过根据其音乐经验和知识向不同级别的用户询问与基本音乐原理有关的问题。我们使用这种方法将音乐构图的最新模型与深度学习进行比较。我们给出了这种评估方法的结果，并比较了每个评估模型的每个用户级别的响应。
Deep learning models are typically evaluated to measure and compare their performance on a given task. The metrics that are commonly used to evaluate these models are standard metrics that are used for different tasks. In the field of music composition or generation, the standard metrics used in other fields have no clear meaning in terms of music theory. In this paper, we propose a subjective method to evaluate AI-based music composition systems by asking questions related to basic music principles to different levels of users based on their musical experience and knowledge. We use this method to compare state-of-the-art models for music composition with deep learning. We give the results of this evaluation method and we compare the responses of each user level for each evaluated model.
